Fleischer Jesper, Hansen Troels Krarup, Cichosz Simon Lebech
Steno Diabetes Center Aarhus, Aarhus, Denmark.
Steno Diabetes Center Zealand, Holbæk, Denmark.
Front Clin Diabetes Healthc. 2022 Dec 9;3:1066744. doi: 10.3389/fcdhc.2022.1066744. eCollection 2022.
This work sought to explore the potential of using standalone continuous glucose monitor (CGM) data for the prediction of hypoglycemia utilizing a large cohort of type 1 diabetes patients during free-living. We trained and tested an algorithm for the prediction of hypoglycemia within 40 minutes on 3.7 million CGM measurements from 225 patients using ensemble learning. The algorithm was also validated using 11.5 million synthetic CGM data. The results yielded a receiver operating characteristic area under the curve (ROC AUC) of 0.988 and a precision-recall area under the curve (PR AUC) of 0.767. In an event-based analysis for predicting hypoglycemic events, the algorithm had a sensitivity of 90%, a lead-time of 17.5 minutes and a false-positive rate of 38%. In conclusion, this work demonstrates the potential of using ensemble learning to predict hypoglycemia, using only CGM data. This could help alarm patients of a future hypoglycemic event so countermeasures can be initiated.
这项研究旨在利用大量1型糖尿病患者在自由生活期间的独立连续血糖监测（CGM）数据，探索其预测低血糖的潜力。我们使用集成学习方法，对来自225名患者的370万次CGM测量数据进行训练和测试，以预测40分钟内的低血糖情况。该算法还使用1150万条合成CGM数据进行了验证。结果显示，曲线下面积（ROC AUC）为0.988，精确召回率曲线下面积（PR AUC）为0.767。在基于事件的低血糖事件预测分析中，该算法的灵敏度为90%，提前时间为17.5分钟，假阳性率为38%。总之，这项研究证明了仅使用CGM数据，利用集成学习预测低血糖的潜力。这有助于向患者发出未来低血糖事件的警报，以便采取应对措施。
